The Physical Activity and Health of Specialists Working with Computers
Bibliographic record
Abstract
The survey on physical activity and health of specialists working with computers was accomplished in Lithuania in 2011. In the survey participated 204 specialists working with computers at the offices, at small and medium sized business companies. Chi-square test was used and the significance level p ≤0.05 was considered statistically significant. The study revealed as about one-third (37.3 %) specialists working with computers everyday feel tired after work, and one-third (34.3 %) after work feel tired 2 - 3 times per week. About half (52%) of specialists working with computers 5-10 hours per day work in sitting position, 35 % work in sitting position 1-5 hours per day, and 11 % work in sitting position more than 10 hours per day. No significant differences between men and women were observed. A half (51%) of specialists working with computers stated as their physical health is good, 34 % - as medium, 11% - as very good. More men than women stated as they have good health (p=0.027). One-third (33%) of specialists working with computers have health problems. Men less than women complain on health problems (p=0.003). The physical activity of working with computers is too low. Both men and women need more attention to the physical activities. The quarter (24%) respondents are taking exercise or go in for sport at least 30 min. per day once per week, 22 % -2-3 per week, 22% -2-3 times per month, 20 % - few times per year or less. Every day at least 0.5 hour per day for physical activity use only 12% respondents. About half of working with computers (58%) agrees, as they should be more physical active. One-third (33%) think, as they have enough knowledge on importance of physical activity to the health, one-third (31%) think as they do not have enough knowledge. When working with computers mostly suffer back, shoulders and neck of the body. Little less, suffer sciatic and lumbar part of the body. Article in Lithuanian doi:10.5200/sm-hs.2012.077
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".